Hepatitis C Virus Infection and Intrinsic Disorder in the Signaling Pathways Induced by Toll-Like Receptors

Simple Summary Human hepatitis C virus causes contagious liver disease hepatitis C, which is broadly spread around the globe. Infection with this virus causes an inflammation of the liver that leads to both acute and chronic hepatitis, which, if left untreated, often results in the development of serious lifelong illnesses such as liver fibrosis, liver cirrhosis, hepatocellular carcinoma, and end-stage liver disease in humans. Toll-like receptors are a class of pattern recognition receptors that exist in liver parenchymal cells and various immune cells, and play an important role in the liver immune system by initiating multiple cellular downstream pathways in response to the recognition of pathogens such as hepatitis C virus. Proteins from the hepatitis C virus, Toll-like receptors themselves, and proteins from the pathways they activate are multifunctional. Since protein multifunctionality is commonly associated with intrinsic disorder (i.e., lack of stable 3D structures), we used an intrinsic disorder angle to examine the interplay between the hepatitis C virus infection and signaling pathways induced by Toll-like receptors. We show that almost all of these proteins contained noticeable levels of disorder, indicating that intrinsic disorder is prominently utilized in virus–host warfare. Abstract In this study, we examined the interplay between protein intrinsic disorder, hepatitis C virus (HCV) infection, and signaling pathways induced by Toll-like receptors (TLRs). To this end, 10 HCV proteins, 10 human TLRs, and 41 proteins from the TLR-induced downstream pathways were considered from the prevalence of intrinsic disorder. Mapping of the intrinsic disorder to the HCV-TLR interactome and to the TLR-based pathways of human innate immune response to the HCV infection demonstrates that substantial levels of intrinsic disorder are characteristic for proteins involved in the regulation and execution of these innate immunity pathways and in HCV-TLR interaction. Disordered regions, being commonly enriched in sites of various posttranslational modifications, may play important functional roles by promoting protein–protein interactions and support the binding of the analyzed proteins to other partners such as nucleic acids. It seems that this system represents an important illustration of the role of intrinsic disorder in virus–host warfare.


Brief Introduction of Human Hepatitis C Virus (HCV)
Human hepatitis C virus (HCV), which causes the contagious liver disease hepatitis C, is one of the best known members of the Hepacivirus genus of the Flaviviridae family of small enveloped RNA viruses. The capsids of these spherical viruses range in diameter between 40 and 60 nm, and their genomes represent single-stranded positive-sense RNA

Introduction of the Protein Intrinsic Disorder Phenomenon
It is recognized that the robustness, multifunctionality, and binding promiscuity of viral proteins can be attributed to their unique structural features and characteristics [42] such as significant enrichment in polar residues and depletion in hydrophobic residues [43], relatively low package density, an increased fraction of residues not involved in secondary structure elements, relatively weak network of inter-residue interactions, lower destabilizing effects of mutations [42], and an abundant presence of partially folded, incompletely ordered, or intrinsically disordered domains and regions that grant structural and functional flexibility to proteins carrying them [44]. This is in line with the current understanding of the protein sequence-function relationship, where protein functionality is not necessarily dependent on a unique 3D-structure, and many biologically active proteins are completely or partially disordered [45][46][47][48][49][50][51][52][53].   [54] and deduced from references [55,56]. The black spheres represent HCV proteins, the white spheres indicate cellular proteins that bind to HCV proteins, and the red spheres signify human proteins that bind to HCV proteins and were implicated in HCV replication by at least one large-scale siRNA screen. The green lines represent interactions between cellular proteins that bind to HCV proteins. The total number of cellular proteins that bind to each HCV protein is shown in parentheses beneath the protein name.
Reproduced from [41] with permission from the Royal Society of Chemistry.
Such functional intrinsically disordered proteins (IDPs) and regions (IDRs) do not have stable 3D structures, being present instead as highly dynamic conformational ensembles [45,47,51,[57][58][59][60]. Intrinsic disorder in proteins comes in multiple flavors, and entire protein or protein regions can be disordered to different degrees, existing as collapsed, semi-collapsed, or extended conformational ensembles with molten globulelike, pre-molten globule-like, and coil-like properties [48,50,60,61]. Overall, any protein can be considered as an ensemble of units capable of independent folding (foldons), IDRs undergoing disorder-to-order transitions induced by binding to specific partners (inducible foldons), IDRs with the capability to fold differently being bound to different partners (morphing inducible foldons), permanently disordered regions (non-foldons), regions with perpetually semi-folded conformations (semi-foldons), and ordered regions, the functional activation of which requires order-to-disorder transitions (unfoldons) [60,[62][63][64]. This structural heterogeneity of proteins is related to their multifunctionality via the structure-function continuum model, which is based on the "one gene-many proteins-many functions" concept, instead of the classical "lock-and-key" theory rooted in the long-accepted "one gene-one protein-one structure-one function" view [65,66]. In fact, IDPs/IDRs are commonly related to the regulation of cell signaling, are involved in a multitude of recognition events, and play important roles in controlling various pathways [47,50,52,60,[67][68][69][70][71][72][73][74][75][76], and thereby complement catalytic and transport activities Figure 1. The interactions between HCV and cellular proteins. HCV-human protein-protein interactions (grey lines) were downloaded from the HCVPro database [54] and deduced from references [55,56]. The black spheres represent HCV proteins, the white spheres indicate cellular proteins that bind to HCV proteins, and the red spheres signify human proteins that bind to HCV proteins and were implicated in HCV replication by at least one large-scale siRNA screen. The green lines represent interactions between cellular proteins that bind to HCV proteins. The total number of cellular proteins that bind to each HCV protein is shown in parentheses beneath the protein name. Reproduced from [41] with permission from the Royal Society of Chemistry.
It is now recognized that IDPs/IDRs are not mere exceptions that are rarely found within the functional universe of ordered proteins, where a unique structure defines the unique function. Instead, they are very abundant in all proteomes analyzed so far [45,46,49,50,53], with the largest variability in the proteome-wide content of disordered residues being found in viral proteomes [83,84]. Recently, the abundance of intrinsic disorder in the completed proteomes of several human HCV genotypes (such as 1a, 1b, 1c, 2a, 2b, 2c, 2k, 3a, 3b, 3k, 4a, 5a, 6a, 6b, 6d, 6g, 6h, and 6k) was evaluated using a wide spectrum of bioinformatic techniques [41]. This analysis was complemented by the investigation of the peculiarities of disorder distribution within the individual HCV proteins, which helped establish a potential conjunction between the structural disorder and functions of the ten HCV proteins [41]. This analysis revealed that "intrinsic disorder or increased flexibility is not only abundant in these proteins, but is absolutely necessary for their functions, playing a crucial role in the proteolytic processing of the HCV polyprotein, the maturation of the individual HCV proteins, and being related to the posttranslational modifications of these proteins and their interactions with DNA, RNA, and various host proteins" [41]. In line with these previous studies, our analysis (see below) showed that HCV contains four ordered proteins (E1, E2, NS2, and NS4B), five moderately disordered proteins (p7, NS3, NS4A, and NS5B), and two highly disordered proteins (core and NS5A).

TLRs, Important Players of the Liver Immune System
Toll-like receptors (TLRs), a class of pattern recognition receptors, exist in liver parenchymal cells and various immune cells, and serve as important members of the liver immune system [20], playing a number of vital roles in both inflammatory and infectious diseases [85,86]. In fact, these receptors are known to improve the overall efficiency of the immune response, being able to mediate innate immunity and induce acquired immunity. In innate immunity, the main functions of TLRs are related to the recognition of danger-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) [87]. Recognition of PAMPS and DAMPs by TLRs initiates a cascade of events that eventually results in the killing of a microbe. The corresponding events range from the recruitment of phagocytes to the site of infection to the initiation of the expression of chemokines and cytokines acting as inflammatory mediators [88,89].
There are ten TLRs (TLR1 through TLR10) in humans [90][91][92][93][94], which are differently distributed within a cell and can be further grouped based on their cellular location, with  TLR1, TLR2, TLR4, TLR5, TLR6, and TLR10 being found on the plasma membrane, and  TLR3, TLR7, TLR8, and TLR9 being located on the endosomal membranes [20,93,94]. Importantly, this diversity of human TLRs is determined by the need to recognize a multitude of the PAMPs from bacteria, fungi, protozoa, and viruses [89,90,93], with each TLR being able to sense a specific set of PAMPs. Here, the nucleic components of pathogens are mainly recognized by the endosomal TLRs, where double-strand RNA, single-stranded RNA, and CpG DNA are sensed by TLR3, TLR7/TLR8, and TLR9, respectively [89,90,[93][94][95]. In response to their corresponding PAMPs, these TLRs stimulate the production of type 1 interferons (IFN1α and IFN1β) [96]. As far as extracellular TLRs are concerned, they can sense a broad range of PAMPs, with TLR5 sensing flagellin and TLR4 recognizing many different PAMPs such as lipopolysaccharides and taxol [89,91]. PAMP-induced activation of these TLRs at the plasma membrane triggers the activation of nuclear factor-κB (NF-κB) and other transcription factors that lead to the expression of several proinflammatory cytokines [97].
TLRs are involved in the interaction with each other (in fact, activation of these receptors is associated with their homo-and heterodimerization) and a multitude of human proteins. This is illustrated by Figure 2, which shows the internal and external proteinprotein interaction (PPI) networks of these proteins. This analysis revealed that TLRs form a densely connected network, where, on average, each protein interacts with at least eight partners. The most connected are TLR1, TLR7, and TLR9, which are expected to interact with all of the remaining TLRs, and the least connected is TLR10, interacting with TLR1, TLR2, TLR3, TLR5, TLR7, and TLR9 (see Figure 2A). As a group, TLRs form an immense PPI network, where one can find 459 proteins (see Figure 2B). In line with these observations, STRING-based analysis of the interactivity revealed that individual TLRs are engaged in multiple PPIs (see Table S1 and corresponding plots in the Supplementary Figure S1).
Biology 2022, 11, x 6 of 28 these observations, STRING-based analysis of the interactivity revealed that individual TLRs are engaged in multiple PPIs (see Table S1 and corresponding plots in the Supplementary Figure S1).  [98]. In the internal network, TLRs are connected by 41 interactions, where each TLR, on average, interacts with eight partners. In the external PPI network, 10 TLRs interact with 449 partners, which are connected by 11,715 binary PPIs. The average node degree of these network is 51 (i.e., each member of this network is expected to interact with 51 partners). Amino acid sequences of the HCV polyprotein genotype 1a (isolate H77), mature individual HCV proteins, human TLRs, and 41 major players of TLR-triggered downstream pathways were retrieved from UniProt (https://www.uniprot.org/ accessed on July 7, 2022) [99]. Corresponding information is included in the Supplementary Figure S1. Search Tool for the Retrieval of Interacting Genes; STRING, http://string-db.org/ accessed on July 7, 2022 [98], was used to obtain information on the interactability of HCV proteins, human TLRs, and 41 major players of TLR-triggered downstream pathways. The STRING output represents a network of predicted and experimentally-validated protein-protein interactions using seven types of evidence such as co-expression evidence (black line), co-occurrence evidence (blue line), neighborhood evidence (green line), database evidence (light blue line), experimental evidence (purple line), fusion evidence (red line), and text mining evidence (yellow line) [98]. The protein-protein interaction networks of all query proteins are shown in the Supplementary Figure S1.
A detailed description of the functional peculiarities of TLRs was outside the scope of this article, and interested readers can find related information in numerous reviews dedicated to this important family of pattern recognition receptors (e.g., see [100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117]). However, the data presented here indicate that TLRs belong to the category of multifunctional proteins. It is likely that this multifunctionality is somehow encoded in the structures of these proteins.  [98]. In the internal network, TLRs are connected by 41 interactions, where each TLR, on average, interacts with eight partners. In the external PPI network, 10 TLRs interact with 449 partners, which are connected by 11,715 binary PPIs. The average node degree of these network is 51 (i.e., each member of this network is expected to interact with 51 partners). Amino acid sequences of the HCV polyprotein genotype 1a (isolate H77), mature individual HCV proteins, human TLRs, and 41 major players of TLR-triggered downstream pathways were retrieved from UniProt (https://www.uniprot.org/ accessed on 7 July 2022) [99]. Corresponding information is included in the Supplementary Figure S1. Search Tool for the Retrieval of Interacting Genes; STRING, http://string-db.org/ accessed on 7 July 2022 [98], was used to obtain information on the interactability of HCV proteins, human TLRs, and 41 major players of TLR-triggered downstream pathways. The STRING output represents a network of predicted and experimentally-validated protein-protein interactions using seven types of evidence such as co-expression evidence (black line), co-occurrence evidence (blue line), neighborhood evidence (green line), database evidence (light blue line), experimental evidence (purple line), fusion evidence (red line), and text mining evidence (yellow line) [98]. The protein-protein interaction networks of all query proteins are shown in the Supplementary Figure S1.

Structure and Intrinsic Disorder in TLRs and Major Players in the TLR-Triggered Cellular Pathways
TLRs are type I (single-pass) transmembrane glycoproteins consisting of three functional domains such as an N-terminal extracellular (or extra-endosomal) leucine rich repeat domain (LRR domain also known as ectodomain) responsible for the recognition of PAMPs and DAMPs, a transmembrane domain (containing an α-helix, which is 21-residue-long in all human TLRs) responsible for the membrane anchoring of TLRs needed for the maintenance of their functional topologies, and a C-terminal intracellular (intraendosomal) Toll/interleukin-1 receptor (TIR) domain with a crucial role in transmitting the extracellular/extraendosomal signals into the cell or endosome [118][119][120]. Ligand binding to ectodomains induces their homo-or heterodimerization, which triggers dimerization of the TIR domains [118]. Activated TLR homo-and heterodimers are characterized by a strikingly similar "M"-like shape, where the C-terminal regions of the ectodomains converge in the middle [118]. This ligand-induced dimerization triggers the recruitment of various adaptor proteins to the intracellular TIR domains of TLRs, thereby initiating corresponding signaling pathways [121].
The LRR domain contains a series of 16-28 repeated LRR modules [122], with each LRR module being 20-30 residues in length and containing a conserved "LxxLxLxxN" motif and a variable part [123,124]. The conserved sequence patterns in the LRR modules define the capability of LRR proteins to fold into the unique horseshoe-like solenoid shape, where the inner concave surface is formed by the "LxxLxLxxN" motives organized in parallel β strands, and the outer convex surface originates from the variable parts of the LRR modules forming α-helices, β-turns, and/or loops [123,124]. Not all LRR modules are made equal, and some LRR proteins contain N-and C-terminally located LRRNT and LRRCT modules that often possess clusters of cysteine residues forming disulfide bridges, but not including LRR motives [123,124].
The TIR domains are~150-residue-long intracellular/intraendosomal TIR domains of TLRs characterized by the common fold, where one can find five α-helices surrounding a five-stranded β-sheet [118]. Based on the results of the mutational and molecular dynamic simulation analyses, it was concluded that the loop connecting the second β-strand and the second α-helix (so-called BB loop) is crucial for the TIR dimerization and/or recruitment of specific adaptor proteins [118,126] such as MyD88, MAL (also known as TIRAP), TRIF, and TRAM [121]. Importantly, since these adaptor proteins also possess TIR domains, the activation of TLR signaling is critically dependent on TIR-TIR interactions between the receptor-receptor, receptor-adaptor, and adaptor-adaptor [127].
Although ordered ectodomains and TIR domains of TLRs are well-studied, there is almost no information on the prevalence and potential functionality of intrinsically disordered and flexible regions in these proteins. To fill this gap, we conducted a multifactorial analysis of the intrinsic disorder predisposition of these important proteins. This analysis revealed that human TLRs contain multiple IDRs, some of which can be relatively long. For example, the longest IDRs in TLR3 and TLR8 have 58 and 59 residues, respectively, the longest IDRs in TLR1 and TLR2 are 39-residues-long, whereas TLR7 has two 33-residuelong IDRs (see Table S1 and the corresponding plots in Supplementary Figure S1). Overall, Table S1 shows that, based on their intrinsic disorder content, human TLRs can be arranged as follows: TLR5 < TLR6 < TLR10 < TLR4 < TLR9 < TLR1 < TLR7 < TLR3 < TLR2 < TLR8. The first four TLRs in this list are expected to be mostly ordered, whereas the remaining six TLRs are predicted as moderately disordered (see Supplementary Table S1). Figure 3 provides the results of the global analysis of the intrinsic disorder predisposition of human TLRs and some major members of the TLR-based signaling cascades. In addition to the utilization of protein disorder status classification based on their PPIDR values (see above), proteins can be classified using their levels of average disorder score (ADS), as highly ordered (ADS < 0.15), moderately disordered (ADS between 0.15 and 0.5), and highly disordered (ADS ≥ 0.5). Figure 3A shows the correlation between the ADS and PPIDR values for the human proteins analyzed in this study. TLRs can be arranged as follows: TLR5 < TLR6 < TLR10 < TLR4 < TLR9 < TLR1 < TLR7 < TLR3 < TLR2 < TLR8. The first four TLRs in this list are expected to be mostly ordered, whereas the remaining six TLRs are predicted as moderately disordered (see Supplementary Table S1). Figure 3 provides the results of the global analysis of the intrinsic disorder predisposition of human TLRs and some major members of the TLR-based signaling cascades. In addition to the utilization of protein disorder status classification based on their PPIDR values (see above), proteins can be classified using their levels of average disorder score (ADS), as highly ordered (ADS < 0.15), moderately disordered (ADS between 0.15 and 0.5), and highly disordered (ADS ≥ 0.5). Figure 3A shows the correlation between the ADS and PPIDR values for the human proteins analyzed in this study.  [128]. Larger values of each parameter correspond to a larger disorder propensity. Different color blocks indicate regions containing proteins with different levels of ordered, where mostly ordered, moderately disordered, and mostly disordered proteins are located within the blue, pink, and red blocks, respectively. If the two parameters (ADS and PPIDR) agree, the corresponding part of the background is shown by a dark color (blue or pink), whereas the light blue and light pink reflect areas in which only one of these criteria applies. (B) CH-CDF plot for 10 human TLRs and 41 major players of the TLR-based signaling. Intrinsic disorder predisposition analysis of all proteins was conducted using RIDAO web crawler, which aggregates the outputs of six well-known disorder predictors: PONDR ® VLXT [129], PONDR ® VL3 [130], PONDR ® VLS2 [131], PONDR ® FIT [132], IUPred2 (Short), and IUPred2 (Long) [133,134], and also provides the mean disorder predictions for query proteins by averaging the outputs of these six predictors. The corresponding disorder profiles of all query proteins are shown in the Supplementary Figure S1. For each query protein, the predicted percentage of intrinsically  [128]. Larger values of each parameter correspond to a larger disorder propensity. Different color blocks indicate regions containing proteins with different levels of ordered, where mostly ordered, moderately disordered, and mostly disordered proteins are located within the blue, pink, and red blocks, respectively. If the two parameters (ADS and PPIDR) agree, the corresponding part of the background is shown by a dark color (blue or pink), whereas the light blue and light pink reflect areas in which only one of these criteria applies. (B) CH-CDF plot for 10 human TLRs and 41 major players of the TLR-based signaling. Intrinsic disorder predisposition analysis of all proteins was conducted using RIDAO web crawler, which aggregates the outputs of six well-known disorder predictors: PONDR ® VLXT [129], PONDR ® VL3 [130], PONDR ® VLS2 [131], PONDR ® FIT [132], IUPred2 (Short), and IUPred2 (Long) [133,134], and also provides the mean disorder predictions for query proteins by averaging the outputs of these six predictors. The corresponding disorder profiles of all query proteins are shown in the Supplementary Figure S1. For each query protein, the predicted percentage of intrinsically disordered residues (PPIDR; i.e., percent of residues with disorder scores exceeding 0.5) was calculated based on the outputs of PONDR ® VLS2, which is characterized by high predictive power, as evidenced by the results of the recently conducted 'Critical assessment of protein intrinsic disorder prediction' (CAID) experiment, where the tool was recognized as predictor #3 of the 43 evaluated methods [135]. Global disorder status of the query proteins was checked by a CH-CDF analysis [136][137][138][139] that combines the outputs of two binary predictors, the charge-hydropathy (CH) plot [51,140] and the cumulative distribution function (CDF) plot [136,140,141], to create a CH-CDF phase space, where proteins are classified as ordered (proteins predicted to be ordered by both binary predictors), putative native "molten globules" or hybrid proteins (proteins determined to be ordered/compact by CH, but disordered by CDF), putative native coils and native pre-molten globules (proteins predicted to be disordered by both methods), and proteins predicted to be disordered by CH-plot, but ordered by CDF.
This analysis revealed that no TLRs can be classified as very structured, since none of these proteins are located within the dark blue region, and only four proteins (TLR4, TLR5, TLR6, and TLR10) can be considered as mostly disordered, with the remaining TLRs being moderately disordered. Further analysis of the global intrinsic disorder predisposition of these proteins is illustrated by Figure 3B, which shows the corresponding charge-hydropathy (CH)-cumulative distribution function (CDF) plot. Here, proteins are classified based on their position within the quadrants of the CH-CDF phase space, where ordered proteins are located within the lower-right quadrant, native molten globules and/or hybrid proteins containing sizable levels of order and disorder are grouped within the lower-left quadrant, and native coils or native pre-molten globules (i.e., highly disordered proteins with extended disorder) are found within the upper-left quadrant [138]. As per the results of this analysis, all TLRs were grouped within the lower-right quadrant, confirming that these proteins were mostly ordered.
To show the peculiarities of the structure and disorder distribution within the human TLRs, we modeled their structures by AlphaFold [142] and also used the D 2 P 2 database to generate functional disorder profiles of these proteins [143]. Figure 4 gives an illustration of this analysis, showing the corresponding results for TLR5 and TLR8 as the most ordered and most disordered human TLRs. Analogous information for other TLRs is presented in the Supplementary Figure S1. Figure 4 shows that proteins that contain both disordered regions and disorder or structural flexibility might have some functional implications (e.g., be involved in posttranslational modifications). The involvement of disorder/flexibility in PTMs is supported by the fact that in TLR5, Asn residues 37, 46, 245, 342, 422, 595, and 598, which are subjected to N-linked glycosylation [144], are characterized by the local disorder scores of 0.051, 0.259, 0.279, 0.146, 0.181, 0.097, and 0.150, respectively, and the phosphorylatable residues Tyr798 [145] and Ser805 [146] showed local disorder scores of 0.411 and 0.328, respectively. In TLR8, the situation was even more dramatic, as Asn residues 29, 42, 80, 88, 115, 160, [147][148][149].
At the next stage, we looked at the intrinsic disorder status of major players of the TLR-initiated signaling cascades. Results of the corresponding analyses are summarized in Table S1, Figure 3, and the corresponding plots in the Supplementary Figure S1 and show that all 41 proteins are expected to contain noticeable levels of intrinsic disorder. In fact, 13 of these proteins are predicted to have more than 50% disordered residues, and another 16 proteins possessed PPIDR values exceeding 25%, whereas disorder content in the remaining 12 proteins ranged from 10.62% (serine/threonine-protein kinase TBK1; TANK-binding kinase 1) to 24.56% (inhibitor of nuclear factor-κB (NF-κB) kinase subunit alpha, IKKα). Similarly, in the CH-CDF plot (see Figure 3B), one of the members of the TLR signaling cascades was predicted as highly disordered, 17 were expected to have a molten globular or hybrid structure, and 23 were mostly ordered.
Biology 2022, 11, x 10 of 28 TLR signaling cascades was predicted as highly disordered, 17 were expected to have a molten globular or hybrid structure, and 23 were mostly ordered.   plots (A,B)) and most disordered (TLR8, plots (C,D)) human TLRs. Structures of these proteins (plots (A,C)) were modeled using AlphaFold [142], which is recognized now as a highly accurate tool suitable for large-scale structure prediction [150]. The modeled structures of all of the query proteins (HCV proteins, human TLRs and 41 major players of TLR-triggered downstream path-ways) are shown in the Supplementary Figure S1. Structural elements in these structures are colored based on the confidence of the structure prediction by AlphaFold, where dark blue and cyan segments correspond to structures predicted with high to very high confidence, whereas yellow and orange segments showed structures predicted with low to very low confidence, and which are expected to be unstructured in isolation. Functional disorder profiles (plots (B,D)) for these proteins were generated using the D 2 P 2 platform (http://d2p2.pro/ accessed on 7 July 2022), which is a database of the predicted disorder for proteins from completely sequenced genomes [143]. Here, the outputs of IUPred [133], PONDR ® VLXT [129], PrDOS [151], PONDR ® VSL2B [130,152], PV2 [143], and ESpritz [153] were used to show the disorder predispositions. Consensus between these nine disorder predictors is shown by the blue-green-white bar, whereas the location of various posttranslational modifications (PTMs) is shown by differently colored circles. The platform also shows the positions of the functional SCOP domains [154,155] predicted by the SUPERFAMILY predictor [156]. Positions of these functional domains are shown below the outputs of nine disorder predictors. The functional disorder profile also includes information on the location of the predicted disorder-based binding sites (MoRF regions) identified by the ANCHOR algorithm [157] and various PTMs assigned using the outputs of the PhosphoSitePlus [158]. The functional disorder profiles of all query proteins are shown in the Supplementary Figure S1.
These four proteins were selected for more in-depth disorder analysis, which is summarized in Figures 5-7. Figure 5 represents the model 3D-structures generated for these proteins by AlphaFold and clearly shows that none of them are expected to have a compact globular structure. Note the absence of the compact globular core in these proteins. Figure 5. The 3D-structures modeled for human IKKγ, c-FOS, Jun, and TANK by AlphaFold [142]. Note the absence of the compact globular core in these proteins.  In fact, C-FOS and Jun are mostly disordered, with each showing a long α-helix related to the formation of the coiled-coil structure in the C-FOS-Jun complex. IKKγ is predicted to have a V-shaped structure with two very long α-helices, whereas TANK, also being highly disordered, is expected to have two disjoined α-helices. Note that none of these proteins have a hydrophobic core, and therefore their long α-helices are unlikely to be stable in the unbound state. Figure 6 represents the functional disorder profiles of human IKKγ, c-FOS, Jun, and TANK generated by the D 2 P 2 platform [143] and shows that these four proteins are massively disordered and densely decorated by multiple various PTMs. Furthermore, they include numerous molecular recognition features (i.e., intrinsically disordered regions that are expected to undergo disorder-to-order transitions at the interaction with specific binding partners). Such features are commonly found within the IDRs of many proteins, where they were shown to play crucial roles in proteinprotein interactions, potentially initiating early steps in molecular recognition [47,48]. IKKγ, c-FOS, Jun, and TANK have 6, 8, 8, and 9 MoRFs that cover 15.75%, 29.21%, 48.04%, In fact, C-FOS and Jun are mostly disordered, with each showing a long α-helix related to the formation of the coiled-coil structure in the C-FOS-Jun complex. IKKγ is predicted to have a V-shaped structure with two very long α-helices, whereas TANK, also being highly disordered, is expected to have two disjoined α-helices. Note that none of these proteins have a hydrophobic core, and therefore their long α-helices are unlikely to be stable in the unbound state. Figure 6 represents the functional disorder profiles of human IKKγ, c-FOS, Jun, and TANK generated by the D 2 P 2 platform [143] and shows that these four proteins are massively disordered and densely decorated by multiple various PTMs. Furthermore, they include numerous molecular recognition features (i.e., intrinsically disordered regions that are expected to undergo disorder-to-order transitions at the interaction with specific binding partners). Such features are commonly found within the IDRs of many proteins, where they were shown to play crucial roles in protein-protein interactions, potentially initiating early steps in molecular recognition [47,48]. IKKγ, c-FOS, Jun, and TANK have 6, 8, 8, and 9 MoRFs that cover 15.75%, 29.21%, 48.04%, and 22.35% of their entire sequences, respectively, indicating that very significant parts of these proteins are involved in molecular recognition.
The fact that significant parts of these four proteins are predicted to be engaged in molecular recognition and undergo binding-induced disorder-to-order transition is further supported by Figure 7, which shows dense PPIs predicted for these four proteins by STRING. Each of these proteins represents a highly connected hub, indicating the heavy use of intrinsic disorder for PPIs. This is in line with previous studies that showed that intrinsic disorder is crucial for the functionality of hubs [52,[159][160][161][162][163][164].
It should be noted that these four proteins are not an exception, and all other participants of TLR signaling analyzed in this study contain multiple MoRFs and act as hubs in densely packed PPI networks (see the Supplementary Figure S1 for this information).
further supported by Figure 7, which shows dense PPIs predicted for these four proteins by STRING. Each of these proteins represents a highly connected hub, indicating the heavy use of intrinsic disorder for PPIs. This is in line with previous studies that showed that intrinsic disorder is crucial for the functionality of hubs [52,[159][160][161][162][163][164].
It should be noted that these four proteins are not an exception, and all other participants of TLR signaling analyzed in this study contain multiple MoRFs and act as hubs in densely packed PPI networks (see the Supplementary Figure S1 for this information).
The internalization of viral PAMPs leads to the activation of the TLR3, TLR7/8, and TLR9 in the endosomes. The activation of TLR3 is associated with the initiation of the MyD88-independent pathways. Similar to TLR4, TLR3 interacts with TRIF, and this leads to the IKKε/TBK-1-driven activation of IRF3. Although TLR7, TLR8, and TLR9 are on the MyD88-dependent pathway, MAL is not needed for their interaction with MyD88. After MyD88 binding, they activate IRAKs that bind to TRAF6, leading to IRF5 and IRF7 (PPIDR = 58.08%) activation and translocation to the nucleus, where these interferon regulatory factors trigger the transcription of anti-inflammatory cytokines.
Finally, the role of TRL10 also needs to be addressed, which, despite being discovered in 2001 [178], is considered to be an orphan receptor whose ligands and functions are poorly known [86,179,180]. Although this receptor can homodimerize and form heterodimers with TLR1, TLR2, and TLR6 [181][182][183][184], the functional significance of the resulting complexes and TLR10 itself remains mostly unknown [182]. It was shown that the TLR10/TLR2 heterodimer and TLR10/TLR10 can bind MyD88, but they are incapable of NF-κB activation [185]. It seems that TLR10 serves as an inhibitor of the MyD88 dependent and independent pathways [86,178].
The data presented in this section are summarized in Figure 8, showing the proteins involved in these TLR pathways as being colored based on their intrinsic disorder status, with highly ordered, moderately disordered, and highly disordered proteins being shown by the blue, pink, and red colors, respectively. With the exception of four TLRs (TLR4, TLR5, TLR6, and TLR10), all the proteins in these networks were either moderately or highly disordered. This once again emphasizes the importance of intrinsic disorder for the regulation and control of these crucial pathways.

TLRs in HCV Infection
Although host cells contain several pattern recognition receptors that can recognize HCV such as C-type lectin receptors (CLRs), cytosolic DNA sensors (CDs), NOD-like receptors (NLRs), RIG-I-like receptors (RLRs), and Toll-like receptors (TLRs) [36], TLRs deserve special attention due to their capability to rapidly establish antiviral response and serve as important players in the process of HCV infection [20]. This is because TLRs can recognize several viral PAMPs and efficiently bind to various HCV structures [20]. A detailed description of the roles of HCV proteins in altering and modulating the TLR-based responses can be found elsewhere [20]. The section below provides a brief overview of this phenomenon from the angle of intrinsic disorder. Figure 9 shows that half of the HCV proteins is tightly related to the TLRs. For example, interactions of the HCV core (PPIDR = 68.18%), NS3 (PPIDR = 22.03%), and NS5A (PPIDR = 47.99%) proteins with TLR1/TLR2 and TLR2/TLR6 activate the downstream TLR2-specific signaling processes in the MyD88-dependent manner (see Figure 8). Here, the TLR2-specific intracellular pathway is utilized by the HCV core and NS3 proteins to activate innate immune and inflammatory cells [186]. Both HCV core and NS3 proteins can be recognized by the TLR2/TLR1 and TLR2/TLR6 heterodimers [187]. Importantly, in chronic HCV infection, the HCV-triggered activation of NF-κB (a complex containing subunits p65 and p50 with PPIDRs of 64.61% and 33.16%, respectively) and AP-1 (a complex of c-Jun (PPIDR = 82.78%) and c-FOS (PPIDR = 83.42%)) transcription factors, and the phosphorylation of JNKs (JNK1, JNK2, and JNK3 with PPIDs of 27.63%, 27.36%, and 26.72%) via the MyD88-driven recruitment of IRAK1 (PPIDR = 52.67%), IRAK4 (PPIDR = 33.48%), and TRAF6 (PPIDR = 22.37%) is believed to be linked to hepatocyte damage, as both hepatocyte metabolism and inflammation/injury/fibrosis are related to the TLR-activated JNKs [188,189]. regulation and control of these crucial pathways.

TLRs in HCV Infection
Although host cells contain several pattern recognition receptors that can recognize HCV such as C-type lectin receptors (CLRs), cytosolic DNA sensors (CDs), NOD-like receptors (NLRs), RIG-I-like receptors (RLRs), and Toll-like receptors (TLRs) [36], TLRs deserve special attention due to their capability to rapidly establish antiviral response and serve as important players in the process of HCV infection [20]. This is because TLRs can recognize several viral PAMPs and efficiently bind to various HCV structures [20]. A detailed description of the roles of HCV proteins in altering and modulating the TLRbased responses can be found elsewhere [20]. The section below provides a brief overview of this phenomenon from the angle of intrinsic disorder. Figure 9 shows that half of the HCV proteins is tightly related to the TLRs. For example, interactions of the HCV core (PPIDR = 68.18%), NS3 (PPIDR = 22.03%), and NS5A (PPIDR = 47.99%) proteins with TLR1/TLR2 and TLR2/TLR6 activate the downstream TLR2specific signaling processes in the MyD88-dependent manner (see Figure 8). Here, the TLR2specific intracellular pathway is utilized by the HCV core and NS3 proteins to activate innate immune and inflammatory cells [186]. Both HCV core and NS3 proteins can be recognized by the TLR2/TLR1 and TLR2/TLR6 heterodimers [187]. Importantly, in chronic HCV infection, the HCV-triggered activation of NF-κB (a complex containing subunits p65 and p50 with PPIDRs of 64.61% and 33.16%, respectively) and AP-1 (a complex of c-Jun (PPIDR = 82.78%) and c-FOS (PPIDR = 83.42%)) transcription factors, and the phosphorylation of JNKs (JNK1, JNK2, and JNK3 with PPIDs of 27.63%, 27.36%, and 26.72%) via the MyD88-driven recruitment of IRAK1 (PPIDR = 52.67%), IRAK4 (PPIDR = 33.48%), and TRAF6 (PPIDR = 22.37%) is believed to be linked to hepatocyte damage, as both hepatocyte metabolism and inflammation/injury/fibrosis are related to the TLRactivated JNKs [188,189]. . Proteins are colored based on their intrinsic disorder status, where highly disordered, moderately disordered, and highly ordered proteins are shown by red, pink, and blue colors, respectively. Proteins are classified based on the accepted strategy rooted in the percent of predicted intrinsic disorder (PPIDR) of query proteins, where proteins are considered as highly ordered, moderately disordered, and highly disordered, if their PPDR <10%, 10% ≤ PPDR < 30%, and PPDR ≥30%, respectively [190].
As was discussed above, TLR4 can activate downstream pathways both in the MyD88dependent and MyD88-independent manner. Although TLR4 activation is typically induced by the lipopolysaccharides (LPSs), HCV NS5A can bind to TLR4 directly, without LPS stimulation, thereby promoting the MyD88-independent activation of the downstream molecules through the pathways related to TRIF (PPIDR = 66.29%) (see Figure 8). This NS5A-TLR4 binding on monocytes leads to an imbalance of inflammatory cytokines enhancing the IL-10 production and decreasing the IL-12 production [191]. This inflammatory cytokine imbalance inhibits the virus-killing effect of the natural killer (NK) cells, thereby helping HCV to evade immune surveillance [191]. Furthermore, NS5A was shown to upregulate the TLR4 expression in the peripheral blood mononuclear cells and B cells, leading to the enhanced production of IFN-β and IL-6 [192]. On the other hand, in hepatocytes, HCV NS5A was shown to downregulate TLR4 expression, thereby inhibiting the LPS-mediated apoptosis of hepatocytes [193].
TLR7/8 can be activated by the HCV single-stranded RNA. It was shown that in the peripheral blood monocytes of the HCV-infected patients, the TLR7 expression was noticeably reduced due to the TLR7 gene distortion and instability of the TLR7 mRNA [197]. Furthermore, NS5A can inhibit the production of some inflammatory factors by binding to the MyD88 death domain, thereby suppressing TLR7/8 signaling [198,199] and affecting other MyD88-dependent pathways downstream of TLRs [20].
Finally, since TLR9 can only bind to DNA, the HCV single-stranded RNA cannot activate this receptor [200]. However, TLR9 can still affect HCV infection via apoptotic cell DNA [201]. Furthermore, similar to TLR7, the levels of the TLR9 mRNA and protein are downregulated in the peripheral blood mononuclear cells from HCV infected patients, being negatively correlated with serum viral copies [202].

Conclusions
In this article, we analyzed the prevalence and potential functional roles of intrinsic disorder in HCV proteins, TLRs, and some major players of the TLR-induced downstream pathways. To the best of our knowledge, this work, being a compilation of the state-ofthe-art and common knowledge on HCV infection and pathophysiological conditions, represents a first systematic study of the intrinsic disorder-based interplay between HCV, TLRs, and TLR-induced downstream pathways. We confirmed that HCV contains four ordered proteins (E1, E2, NS2, and NS4B), five moderately disordered proteins (p7, NS3, NS4A, and NS5B), and two highly disordered proteins (core and NS5A). We also showed that based on their intrinsic disorder content, human TLRs can be arranged as follows: TLR5 < TLR6 < TLR10 < TLR4 < TLR9 < TLR1 < TLR7 < TLR3 < TLR2 < TLR8. The first four TLRs in this list are expected to be mostly ordered, whereas the remaining six TLRs were predicted as moderately disordered. Analysis of the 41 TLR signaling-related proteins revealed that they contained high levels of intrinsic disorder that ranged from 10.62% in TBK1 to 98.75% in IKKγ (NEMO). Of these proteins, 13 are expected to have more than 50% disordered residues, 16 proteins possess PPIDR values between 25% and 50%, and the remaining 12 proteins have disorder content that ranges from 10.62% (TBK1) to 24.56% (IKKα). In all HCV, TLRs, and TLR signaling proteins analyzed in this study, disordered residues are assembled into the functional IDRs, which serve either as a signal for a variety of posttranslational modifications or are used for protein-protein interactions, often being capable of undergoing the binding-induced folding at interaction with specific partners.
The fact that most of these proteins have intrinsic disorder, are capable of undergoing functional disorder-to-order transitions, and can be subjected to multiple PTMs indicates that each of them, despite being encoded by a single gene, exists as a dynamic ensemble of structurally and functionally distinct protein molecules, known as proteoforms [65,203]. Therefore, the functionality of these proteins can be described within the frames of the "protein structure-function continuum" model, where the structure of a protein represents a highly dynamic conformational ensemble containing multiple proteoforms that have different structural features and might have various functions [204][205][206]. Such disorderbased structural and functional heterogeneity of HCV proteins, TLRs, and proteins from the TLR pathways are important for a better understanding of both the HCV pathogenesis and the innate immune response to the HCV infection.
These observations are in line with the well-known association between the protein intrinsic disorder and pathogenesis of various human diseases [81,[207][208][209][210][211][212]. They also agree with the results of the comprehensive bioinformatics analyses of the prevalence of protein intrinsic disorder in various viruses such as human papillomaviruses (HPVs) [213], HCV [41], influenza viruses [214], HIV-1 [215], Dengue virus [216], respiratory syncytial virus (RSD) [217], Zika virus [218,219], Chikungunya virus [220,221], rotavirus [222], Japanese encephalitis virus [223], SARS-CoV-2 [224,225], human SARS and bat SARS-like coronaviruses [224], Middle East respiratory syndrome MERS coronavirus [226,227], and Chandipura virus [228] as well as in the interactomes of HPV [229] and HCV [40]. They are also supported by the results of the evaluation of intrinsic disorder in proteins involved in innate anti-viral immunity [230] and the comprehensive bioinformatics analysis of the global prevalence of intrinsic disorder in 6108 viral proteomes [231]. Our study emphasizes the importance of the systematic analysis of intrinsic disorder for a better understanding of the pathogenesis of viral infections. Furthermore, this work provides important information that can be utilized in the future development of the novel therapy against HCV hepatitis.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/biology11071091/s1, Figure S1: Amino acid sequences, structural and intrinsic disorder-based features of HCV proteins, human TLRs, and major players of the TLR-regulated downstream signaling pathways; Table S1: Some physico-chemical and intrinsic disorder-related features of HCV proteins, human TLRs, and major players of the TLR-regulated downstream signaling pathways.